DocumentCode
3118975
Title
A Neural Approach to Active Estimation of Nonlinear Systems
Author
Baglietto, M. ; Garassino, D. ; Scardovi, L. ; Zanchi, L. ; Zoppoli, R.
fYear
2005
fDate
12-15 Dec. 2005
Firstpage
4511
Lastpage
4517
Abstract
In this paper, we consider the problem of actively identifying the state of a stochastic dynamic system over a finite horizon. We formalize this Problem as a Stochastic Optimal Control one, in which the minimization of a suitable uncertainty measure is performed. To this end, the use of the Renyi Entropy is proposed and motivated. A neural control scheme, based on the application of the Extended Ritz Method and on the use of a Gaussian Sum Filter, is then presented. Simulation results show the effectiveness of the approach.
Keywords
Control systems; Cost function; Entropy; Measurement uncertainty; Nonlinear systems; Optimal control; Performance evaluation; State estimation; Stochastic processes; Stochastic systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Print_ISBN
0-7803-9567-0
Type
conf
DOI
10.1109/CDC.2005.1582873
Filename
1582873
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